For this project, we looked at the Environmental Protection Agency’s fuel economy data for every vehicle tested at the National Vehicle and Fuel Emissions Laboratory in Ann Arbor, Michigan since 1984. This dataset has data on almost 37,000 different vehicles, and includes many attributes to analyze. The shiny app resides here on a shiny server that anybody can access. It may take up to a minute for the plots to load up, and you may have to move the mouse over the plot area to get the plots to appear. Also, if it says “Empty Reply from the server”, just refresh the webpage. The server storing the database that has the data is not that robust.
require("jsonlite")## Loading required package: jsonlite
##
## Attaching package: 'jsonlite'
##
## The following object is masked from 'package:utils':
##
## Viewrequire("RCurl")## Loading required package: RCurl
## Loading required package: bitops# Loads the data from Fast Food table into Fast Food dataframe
# Change the USER and PASS below to be your UTEid
vehicles <- data.frame(fromJSON(getURL(URLencode('skipper.cs.utexas.edu:5001/rest/native/?query="select ATVTYPE,BARRELS08,BARRELSA08,CITY08,CITYA08,CO2TAILPIPEAGPM,CO2TAILPIPEGPM,COMB08,COMBA08,CYLINDERS,FUELCOST08,FUELCOSTA08,FUELTYPE,FUELTYPE1,FUELTYPE2,HIGHWAY08,HIGHWAYA08,HLV,HPV,LV2,LV4,MPGDATA,PV2,PV4,YEAR,MAKE,TRANY from VEHICLES"'),httpheader=c(DB='jdbc:oracle:thin:@sayonara.microlab.cs.utexas.edu:1521:orcl', USER='C##cs329e_sm44585', PASS='orcl_sm44585', MODE='native_mode', MODEL='model', returnDimensions = 'False', returnFor = 'JSON'), verbose = TRUE)))
summary(vehicles)## ATVTYPE BARRELS08 BARRELSA08 CITY08
## null :33825 Min. : 0.0599 Min. : 0.0000 Min. : 6.00
## FFV : 1280 1st Qu.:14.9643 1st Qu.: 0.0000 1st Qu.: 15.00
## Diesel : 1018 Median :17.3375 Median : 0.0000 Median : 17.00
## Hybrid : 416 Mean :17.6101 Mean : 0.2144 Mean : 17.81
## EV : 95 3rd Qu.:20.5896 3rd Qu.: 0.0000 3rd Qu.: 20.00
## CNG : 50 Max. :47.0683 Max. :18.3043 Max. :138.00
## (Other): 66
## CITYA08 CO2TAILPIPEAGPM CO2TAILPIPEGPM COMB08
## Min. : 0.0000 Min. : 0.00 Min. : 0.0 Min. : 7.00
## 1st Qu.: 0.0000 1st Qu.: 0.00 1st Qu.: 401.0 1st Qu.: 16.00
## Median : 0.0000 Median : 0.00 Median : 467.7 Median : 19.00
## Mean : 0.4855 Mean : 17.77 Mean : 475.5 Mean : 20.05
## 3rd Qu.: 0.0000 3rd Qu.: 0.00 3rd Qu.: 555.4 3rd Qu.: 23.00
## Max. :127.0000 Max. :719.00 Max. :1269.6 Max. :124.00
##
## COMBA08 CYLINDERS FUELCOST08 FUELCOSTA08
## Min. : 0.000 4 :14010 Min. : 500 Min. : 0.00
## 1st Qu.: 0.000 6 :12838 1st Qu.:1600 1st Qu.: 0.00
## Median : 0.000 8 : 8099 Median :1950 Median : 0.00
## Mean : 0.546 5 : 766 Mean :1968 Mean : 91.73
## 3rd Qu.: 0.000 12 : 533 3rd Qu.:2300 3rd Qu.: 0.00
## Max. :117.000 3 : 200 Max. :5900 Max. :4000.00
## (Other): 304
## FUELTYPE FUELTYPE1 FUELTYPE2
## Regular :24592 Diesel : 1090 E85 : 1280
## Premium : 9506 Electricity : 95 Electricity: 38
## Gasoline or E85: 1164 Midgrade Gasoline: 61 Natural Gas: 20
## Diesel : 1090 Natural Gas : 60 null :35404
## Premium or E85 : 116 Premium Gasoline : 9645 Propane : 8
## Electricity : 95 Regular Gasoline :25799
## (Other) : 187
## HIGHWAY08 HIGHWAYA08 HLV HPV
## Min. : 9.00 Min. : 0.0000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 20.00 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 24.00 Median : 0.0000 Median : 0.000 Median : 0.00
## Mean : 23.95 Mean : 0.6506 Mean : 2.036 Mean : 10.49
## 3rd Qu.: 27.00 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.00
## Max. :111.00 Max. :107.0000 Max. :49.000 Max. :195.00
##
## LV2 LV4 MPGDATA PV2
## Min. : 0.000 Min. : 0.00 N:25512 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 0.00 Y:11238 1st Qu.: 0.00
## Median : 0.000 Median : 0.00 Median : 0.00
## Mean : 1.856 Mean : 6.19 Mean : 13.68
## 3rd Qu.: 0.000 3rd Qu.:13.00 3rd Qu.: 0.00
## Max. :41.000 Max. :55.00 Max. :194.00
##
## PV4 YEAR MAKE TRANY
## Min. : 0.00 Min. :1984 Chevrolet: 3705 Automatic 4-spd:11039
## 1st Qu.: 0.00 1st Qu.:1990 Ford : 3042 Manual 5-spd : 8283
## Median : 0.00 Median :2000 Dodge : 2498 Automatic 3-spd: 3151
## Mean : 33.74 Mean :2000 GMC : 2345 Automatic (S6) : 2413
## 3rd Qu.: 90.00 3rd Qu.:2009 Toyota : 1863 Manual 6-spd : 2326
## Max. :192.00 Max. :2016 BMW : 1600 Automatic 5-spd: 2184
## (Other) :21697 (Other) : 7354The Shiny app is constructed from a server r script and a ui r script. The UI is a dashboard with four tabs:
require(jsonlite)
require(RCurl)
require(ggplot2)
require(dplyr)
require(reshape2)
require(shiny)
shinyServer(function(input, output) {
#Code to generate data frame
vehicles <- eventReactive(c(input$refreshData), {
vehicles <- data.frame(fromJSON(getURL(URLencode('skipper.cs.utexas.edu:5001/rest/native/?query="select ATVTYPE,BARRELS08,BARRELSA08,CITY08,CITYA08,CO2TAILPIPEAGPM,CO2TAILPIPEGPM,COMB08,COMBA08,CYLINDERS,FUELCOST08,FUELCOSTA08,FUELTYPE,FUELTYPE1,FUELTYPE2,HIGHWAY08,HIGHWAYA08,HLV,HPV,LV2,LV4,MPGDATA,PV2,PV4,YEAR,MAKE,TRANY from VEHICLES"'),httpheader=c(DB='jdbc:oracle:thin:@sayonara.microlab.cs.utexas.edu:1521:orcl', USER='C##cs329e_sm44585', PASS='orcl_sm44585', MODE='native_mode', MODEL='model', returnDimensions = 'False', returnFor = 'JSON'), verbose = TRUE)))
}, ignoreNULL = FALSE)
#Code that generates reactive transmission filter for the bar chart.
trans_filter <- eventReactive(c(input$refreshAll, input$BarPlot), {
if (input$TRANY == "All"){
trans_filter = c("Automatic 3-spd", "Automatic 4-spd","Automatic 5-spd","Automatic 6-spd","Automatic 6spd","Automatic 7-spd", "Automatic 8-spd", "Automatic 9-spd", "Manual 3-spd", "Manual 4-spd", "Manual 5-spd", "Manual 5 spd", "Manual 6-spd", "Manual 7-spd")
}
else {
trans_filter = input$TRANY
}
}, ignoreNULL = FALSE)
#Code that generate reactive year selector for the scatter plot.
year_range <- eventReactive(c(input$refreshAll, input$ScatterPlot), {
if (input$BEG_YEAR <= input$END_YEAR){
year_range = input$BEG_YEAR:input$END_YEAR
}
else {
year_range = 1985:2016
}
}, ignoreNULL = FALSE)
#Code that generates reactive KPI inputs for the PV2 crosstab
MPG_PV2_KPI_LOW <- eventReactive(c(input$refreshAll, input$PV2Plot), {MPG_PV2_KPI_LOW_2 = input$PV2_KPILow}, ignoreNULL = FALSE)
MPG_PV2_KPI_HIGH <- eventReactive(c(input$refreshAll, input$PV2Plot), {MPG_PV2_KPI_HIGH_2 = input$PV2_KPIHigh}, ignoreNULL = FALSE)
#Code that generates reactive KPI inputs for the PV4 crosstab
MPG_PV4_KPI_LOW <- eventReactive(c(input$refreshAll, input$PV4Plot), {MPG_PV2_KPI_LOW = input$PV4_KPILow }, ignoreNULL = FALSE)
MPG_PV4_KPI_HIGH <- eventReactive(c(input$refreshAll, input$PV4Plot), {MPG_PV2_KPI_HIGH = input$PV4_KPIHigh }, ignoreNULL = FALSE)
#Code to generate PV2 Crosstab plot
output$crosstabPV2Plot <- renderPlot({
crosstab <- vehicles() %>% group_by(MAKE, YEAR) %>% summarize(sum_comb08 = sum(COMB08), sum_pv2 = sum(PV2),sum_pv4 = sum(PV4)) %>% mutate(ratio_1 = sum_comb08 / (sum_pv2))%>% mutate(ratio_2 = sum_comb08 / (sum_pv4)) %>% mutate(kpi_1 = ifelse(ratio_1 < MPG_PV2_KPI_LOW(), '03 Not Efficient or Spacious', ifelse(ratio_1 <= MPG_PV2_KPI_HIGH(), '02 Average Efficiency and Space', '01 Efficient and Spacious')))%>% mutate(kpi_2 = ifelse(ratio_2 < MPG_PV2_KPI_LOW(), '03 Not Efficient or Spacious', ifelse(ratio_2 <= MPG_PV2_KPI_HIGH(), '02 Average Efficiency and Space', '01 Efficient and Spacious'))) %>%filter(MAKE %in% c("Acura", "Aston Martin", "Audi", "Bentley", "BMW", "Buick", "Chevrolet", "Dodge", "Ferrari", "Ford", "Honda", "Kia", "Lincoln", "Lexus", "Maserati", "Mazda", "Mercedes-Benz", "Nissan", "Toyota", "Volkswagen")) %>% filter(ratio_1 != Inf, ratio_2 != Inf)
# This line turns the make and year columns into ordered factors.
crosstab <- crosstab %>% transform(MAKE = ordered(MAKE), YEAR = ordered(YEAR))
#This generates the PV4 with combined MPG plot
plot <-ggplot() +
coord_cartesian() +
scale_x_discrete() +
scale_y_discrete() +
labs(title='Vehicle Crosstab of Efficiency/Space ratio for 2 door cars') +
labs(x=paste("Make"), y=paste("Year")) +
layer(data=crosstab,
mapping=aes(x=MAKE, y=YEAR, label=round(ratio_1, 2)),
stat="identity",
stat_params=list(),
geom="text",
geom_params=list(colour="black"),
position=position_identity()
) +
layer(data=crosstab,
mapping=aes(x=MAKE, y=YEAR, fill=kpi_1),
stat="identity",
stat_params=list(),
geom="tile",
geom_params=list(alpha=0.50),
position=position_identity()
)
# End your code here.
return(plot)
})
#Code to generate PV4 Crosstab plot
output$crosstabPV4Plot <- renderPlot({
crosstab <- vehicles() %>% group_by(MAKE, YEAR) %>% summarize(sum_comb08 = sum(COMB08), sum_pv2 = sum(PV2),sum_pv4 = sum(PV4)) %>% mutate(ratio_1 = sum_comb08 / (sum_pv2))%>% mutate(ratio_2 = sum_comb08 / (sum_pv4)) %>% mutate(kpi_1 = ifelse(ratio_1 < MPG_PV4_KPI_LOW(), '03 Not Efficient or Spacious', ifelse(ratio_1 <= MPG_PV4_KPI_HIGH(), '02 Average Efficiency and Space', '01 Efficient and Spacious')))%>% mutate(kpi_2 = ifelse(ratio_2 < MPG_PV4_KPI_LOW(), '03 Not Efficient or Spacious', ifelse(ratio_2 <= MPG_PV4_KPI_HIGH(), '02 Average Efficiency and Space', '01 Efficient and Spacious'))) %>%filter(MAKE %in% c("Acura", "Aston Martin", "Audi", "Bentley", "BMW", "Buick", "Chevrolet", "Dodge", "Ferrari", "Ford", "Honda", "Kia", "Lincoln", "Lexus", "Maserati", "Mazda", "Mercedes-Benz", "Nissan", "Toyota", "Volkswagen")) %>% filter(ratio_1 != Inf, ratio_2 != Inf)
# This line turns the make and year columns into ordered factors.
crosstab <- crosstab %>% transform(MAKE = ordered(MAKE), YEAR = ordered(YEAR))
#This generates the PV4 with combined MPG plot
plot <-ggplot() +
coord_cartesian() +
scale_x_discrete() +
scale_y_discrete() +
labs(title='Vehicle Crosstab of Efficiency/Space ratio for 4 door cars') +
labs(x=paste("Make"), y=paste("Year")) +
layer(data=crosstab,
mapping=aes(x=MAKE, y=YEAR, label=round(ratio_2, 2)),
stat="identity",
stat_params=list(),
geom="text",
geom_params=list(colour="black"),
position=position_identity()
) +
layer(data=crosstab,
mapping=aes(x=MAKE, y=YEAR, fill=kpi_2),
stat="identity",
stat_params=list(),
geom="tile",
geom_params=list(alpha=0.50),
position=position_identity()
)
# End your code here.
return(plot)
})
#Code to generate Bar Chart Plot
output$barchartPlot <- renderPlot({
bar_chart <- vehicles() %>% select(TRANY, HIGHWAY08, CITY08) %>% subset(TRANY %in% trans_filter()) %>% group_by(TRANY) %>% summarise(avg_city_MPG = mean(CITY08), avg_highway_MPG = mean(HIGHWAY08)) %>% melt(id.vars = c("TRANY")) %>% group_by(variable) %>% mutate(WINDOW_AVG_MPG = mean(value))
#Plot Function to generate bar chart with reference line and values
plot <- ggplot() +
coord_cartesian() +
scale_x_discrete() +
scale_y_continuous() +
facet_wrap(~variable) +
labs(title='Average Highway and City MPG based on transmission ') +
labs(x=paste("Transmission"), y=paste("MPG")) +
layer(data=bar_chart,
mapping=aes(x=TRANY, y=value),
stat="identity",
stat_params=list(),
geom="bar",
geom_params=list(colour="blue", fill="white"),
position=position_dodge()
) + coord_flip() +
layer(data=bar_chart,
mapping=aes(x=TRANY, y=value, label=round(WINDOW_AVG_MPG, 2)),
stat="identity",
stat_params=list(),
geom="text",
geom_params=list(colour="black", hjust=2),
position=position_identity()
) +
layer(data=bar_chart,
mapping=aes(yintercept = WINDOW_AVG_MPG),
geom="hline",
geom_params=list(colour="red")
) +
layer(data=bar_chart,
mapping=aes(x=TRANY, y=value, label=round(value, 2)),
stat="identity",
stat_params=list(),
geom="text",
geom_params=list(colour="black", hjust=0),
position=position_identity()
)
return(plot)
})
#Code to generate the scatter plot
output$ScatterPlot <- renderPlot({
scatterplot <- vehicles() %>% select(COMB08, YEAR) %>% subset(YEAR %in% year_range()) %>% transform(YEAR = as.Date(as.character(YEAR), "%Y"))
plot <- ggplot() +
coord_cartesian() +
scale_x_date() +
scale_y_continuous() +
labs(title="Combined MPG of every model year") +
labs(x="Year", y="Combined MPG") +
layer(data=scatterplot ,
mapping=aes(x=YEAR, y=COMB08),
stat="identity",
stat_params=list(),
geom="point",
geom_params=list(),
position=position_identity()
)
return(plot)
})
})This script generates all of the output and handles the inputs for our shiny app. The architecture is as follows: data is gathered once from Oracle when the app is loaded and all user input only changes the local copy of the data frame. In other words, the data is stored in the local R session, which allows for a more fluid and responsive application for the user.
This plot looks at how efficient and spacious a car manufacturers portfolio of vehicles happens to be. The lower the KPI, the less efficency relative to space a manufacturer offers. A higher KPI indicates that the manufacturer offers vehicles that are not only spacious but also fuel efficient. The server script for this plot is wrapped in an eventReactive function that fires only when the user presses the “Generate PV2 Plot” after changing the two PV2 KPI slider inputs.
This plot looks at how efficient and spacious a car manufacturers portfolio of vehicles happens to be. The lower the KPI, the less efficency relative to space a manufacturer offers. A higher KPI indicates that the manufacturer offers vehicles that are not only spacious but also fuel efficient. The server script for this plot is wrapped in an eventReactive function that fires only when the user presses the “Generate PV4 Plot” after changing the two PV4 KPI slider inputs.
This plot looks at the city and highway MPG for 14 different transmission types. This allows the user to see what transmission gets the best MPG. The server script for this plot is wrapped in an eventReactive function that fires only when the user presses the “Generate Bar Chart” after clicking on the various transmissions that the user wants to look at.
This plot looks at the combined MPG of all cars in different year ranges since 1985 to 2016. This plot was created to see if a trend was present. The server script for this plot is wrapped in an eventReactive function that fires only when the user presses the “Generate Scatter Plot” after changing the begining year and end year slider inputs that the user wants to look at.
require(shiny)
require(shinydashboard)
require(leaflet)
# Define UI for application that plots random distributions
dashboardPage(
dashboardHeader(title = "Project 6: EPA MPG Data Visualization", titleWidth = 400
),
dashboardSidebar(
sidebarMenu(
menuItem("Crosstab PV2", tabName = "PV2_Crosstab", icon = icon("th")),
menuItem("Crosstab PV4", tabName = "PV4_Crosstab", icon = icon("th")),
menuItem("Bar Chart", tabName = "Bar_Chart", icon = icon("bar-chart")),
menuItem("Scatterplot", tabName = "Scatterplot", icon = icon("th")),
menuItem("Refresh All Plots and Data", tabName = "Refresh", icon = icon("database"))
)
),
dashboardBody(
tabItems(
# First tab content
tabItem(tabName = "PV2_Crosstab",
sliderInput("PV2_KPILow",
"Maximum Low Value for PV2 KPI:",
min = 0,
max = 1,
value = 1),
#Slider for PV2 KPI2
sliderInput("PV2_KPIHigh",
"Maximum Medium Value for PV2 KPI:",
min = 1,
max = 2,
value = 2),
actionButton("PV2Plot", "Generate PV2 Crosstab Plot"),
plotOutput("crosstabPV2Plot")
),
tabItem(tabName = "PV4_Crosstab",
sliderInput("PV4_KPILow",
"Maximum Low Value for PV4 KPI:",
min = 0,
max = 1,
value = 1),
#Slider for PV4 KPI2
sliderInput("PV4_KPIHigh",
"Maximum Medium Value for PV2 KPI:",
min = 1,
max = 2,
value = 2),
actionButton("PV4Plot", "Generate PV4 Crosstab Plot"),
plotOutput("crosstabPV4Plot")
),
tabItem(tabName = "Bar_Chart",
checkboxGroupInput(inputId = "TRANY",
label ="Transmission:",
choices = c("All", "Automatic 3-spd", "Automatic 4-spd","Automatic 5-spd","Automatic 6-spd","Automatic 6spd","Automatic 7-spd", "Automatic 8-spd", "Automatic 9-spd", "Manual 3-spd", "Manual 4-spd", "Manual 5-spd", "Manual 5 spd", "Manual 6-spd", "Manual 7-spd"), selected = "All", inline = TRUE
),
#action button to generate bar chart plot
actionButton("BarPlot", "Generate Bar Plot"),
plotOutput("barchartPlot")
),
tabItem(tabName = "Scatterplot",
p("Note: if beginning year is greater than end year, the chart defaults to displaying all model years."),
#data range slider for each year
sliderInput("BEG_YEAR",
"Pick a beginning year of model year:",
min = 1985,
max = 2016,
value = 1985,
step = 1,
sep = ""),
sliderInput("END_YEAR",
"Pick an end year of model year:",
min = 1985,
max = 2016,
value = 2016,
step = 1,
sep = ""),
actionButton("ScatterPlot", "Generate Scatter Plot"),
plotOutput("ScatterPlot")
),
tabItem(tabName = "Refresh",
#action button to generate plots
br(),br(),
p("If you want to change inputs for multiple charts but don't want to push each button to update the chart, click the button below to refresh all of the plots at once."),
actionButton("refreshAll", "Refresh All Plots"),
br(),br(),
p("If you want to refresh the data from Oracle, click the button below"),
actionButton("refreshData", "Refresh the Data")
)
)
))This script handles all of the rendering and the user interface for the application. The architecture is a dashboard with 5 tabs. The first tab contains a crosstab that looks at 2 door passenger cars with two slider inputs for the user to determine what threshold he/she may want to set for the KPI’s. The second tab contains another crosstab simliar to the first tab but looks at 4 door passenger cars. The third tab has a Bar Chart with 14 checkboxes that correspond to the 14 different types of transmissions. The fourth tab is a scatterplot that looks at the combined MPG for all vehicles in a given model year and has two inputs: one allows the user to select the beginning model year for visualizing and the other selects the end year. The last tab has two buttons: one button called “Refresh All Plots” that will, when clicked, refresh all 4 plots based on user inputs without getting data from Oracle. The other button called “Refresh the Data” gets a new copy of the data from Oracle.
This plot looks at how efficient and spacious a car manufacturers portfolio of vehicles happens to be. The lower the KPI, the less efficency relative to space a manufacturer offers. A higher KPI indicates that the manufacturer offers vehicles that are not only spacious but also fuel efficient. The UI script is wrapped in a tabItem function as the first tab and includes one input slider for determining the maximum low KPI value, one input slider for determining the maximum medium KPI value, and one action button that, when clicked, regenerates the plot with the users input.
This plot looks at how efficient and spacious a car manufacturers portfolio of vehicles happens to be. The lower the KPI, the less efficency relative to space a manufacturer offers. A higher KPI indicates that the manufacturer offers vehicles that are not only spacious but also fuel efficient. The UI script is wrapped in a tabItem function as the second tab and includes one input slider for determining the maximum low KPI value, one input slider for determining the maximum medium KPI value, and one action button that, when clicked, regenerates the plot with the users input.
This plot looks at the city and highway MPG for 14 different transmission types. This allows the user to see what transmission gets the best MPG. The UI script is wrapped in a tabItem function as the third tab and includes a groupedCheckbox input of 14 checkboxes, each with a unique type of transmission for the user to select.
This plot looks at how the combined MPG of all cars since 1985 has evolved over the years. This plot was created to see if a trend was present. The UI script is wrapped in a tabItem function as the last tab and includes one input slider for determining starting year that the user wants to start, one input slider for determining end year that the user wants to stop, and one action button that, when clicked, regenerates the plot with the users input.